TY - GEN
T1 - Cost- And Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
AU - Jung, Giju
AU - Fouda, Mohammed
AU - Lee, Sugil
AU - Lee, Jongeun
AU - Eltawil, Ahmed
AU - Kurdahi, Fadi
N1 - KAUST Repository Item: Exported on 2021-08-11
Acknowledgements: This work was supported by NRF grants funded by MSIT of Korea (No. 2016M3A7B4909668, No. 2017R1D1A1B03033591, and No. 2020R1A2C2015066), IITP grant funded by MSIT of Korea (No.2020-0-01336, Artificial Intelligence Graduate School Program (UNIST)), and Free Innovative Research Fund of UNIST (1.170067.01). The EDA tool was supported by the IC Design Education Center (IDEC), Korea. J. Lee is the corresponding author of this paper (Email: [email protected]).
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a dataset-free, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20 % fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop.
AB - Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a dataset-free, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20 % fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop.
UR - http://hdl.handle.net/10754/670546
UR - http://www.scopus.com/inward/record.url?scp=85111022423&partnerID=8YFLogxK
U2 - 10.23919/DATE51398.2021.9474226
DO - 10.23919/DATE51398.2021.9474226
M3 - Conference contribution
SN - 9783981926354
SP - 1733
EP - 1738
BT - 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
ER -